Biology subjects

Lechat, P.

Publications and source records attributed to Lechat, P..

8 recordsLinked to original sources

bioRxiv2024

Revisiting oxygen toxicity: evolution and adaptation to superoxide in a SOD-deficient bacterial pathogen

Defenses against oxidants are crucial for the virulence of pathogens, with superoxide scavenging enzymes (SOSEs) playing a vital role for most aerobes. However, our knowledge of superoxide adaptation primarily stems from the study of SOSE-encoding bacteria. Here, we investigated the evolution of a naturally SOSE-deficient pathogen (Leptospira spp.), along with the alternative mechanisms it recruits to combat superoxide stress. We demonstrate that emergence of pathogenic Leptospira correlated with SOD loss, but that a long-lasting adaptation to superoxide remains possible. We reveal that cysteine and leucine biosynthesis are the most induced pathways in response to superoxide and demonstrate the importance of sulfur metabolism in superoxide adaptation in this SOSE-deficient model. We also propose cysteine oxidation as a key mediator of superoxide toxicity in the absence of SOSEs. This study challenges our conventional understanding of the oxygen toxicity theory and proposes a new model of superoxide adaptation through metabolic rewiring in bacteria.

microbiology↗

bioRxiv2024

A type IVB secretion system contributes to the pathogenicity of Yersinia pseudotuberculosis strains responsible for the Far East scarlet-like fever

Yersinia pseudotuberculosis is a food-borne pathogen responsible for a self-limiting gastrointestinal disease in humans known as mesenteric lymphadenitis. A phylogenetically distinct Y. pseudotuberculosis cluster from lineages 1 and 8 is associated to a specific syndrome called the Far East scarlet-like fever (FESLF), characterized by skin rash, hyperemic tongue and desquamation. Genome sequencing of FESLF strains previously revealed the presence in the plasmid pVM82 of dot/icm genes, homologous to those known to encode a T4BSS in the intracellular pathogens Legionella pneumophila and Coxiella burnetii. In the present article, we characterized the genomic features and functionality of the Y. pseudotuberculosis T4BSS (yT4BSS). We found higher dot/icm gene identity between Y. pseudotuberculosis and Pseudomonas putida genes than with those of L. pneumophila or C. burnetii. We validated the presence of all essential dot/icm genes required for the structure of a T4BSS. We then evaluated the conditions required for yT4BSS gene expression in vitro and identified an influence of temperature, with higher expression at 37{degrees}C, which mimicks the mammalian host temperature. The yT4BSS is also expressed in cellulo during the Y. pseudotuberculosis intracellular life cycle and in vivo during mouse infection. Although T4BSS functions are well characterized in the intracellular life cycle of L. pneumophila and C. burnetii, the yT4BSS appears to not be required for the intracellular survival nor for the establishment of a replication niche within cells of Y. pseudotuberculosis. Interestingly, the yT4BSS is implicated in Y. pseudotuberculosis FESLF strain pathogenicity when orally inoculated to mice but not during intravenous inoculation. Despite a role in virulence during oral infection, the yT4BSS does not influence organ colonization. However, the yT4BSS appears to be implicated in induction of important necrosis lesions in mesenteric lymph nodes and caeca of mice. Cytokine profil analyses revealed an induction of production of innate immunity related cytokines and chemokines depending on the yT4BSS in cellulo using a mouse bone marrow-derived macrophages infection model. Thus, the yT4BSS modulates cytokine responses of the host innate immune system during oral infection. In conclusion, the yT4BSS is a newly characterized virulence factor implicated in pathogenicity of Y. pseudotuberculosis strains from lineage 8 responsible for FESLF.

microbiology↗

bioRxiv2024

Impact Of Learned Helplessness On Cognitive Performance And Resting-State Connectivity: An fMRI Study

1.1Learned helplessness (LH) is the phenomenon of resignation in the face of a problematic situation and is caused by the feeling of lacking control in a situation due to internal or external factors. This means that an individual does not seek to resolve the situation they are confronted with and consciously or unconsciously chooses to be passive. The Dorsal Raphe Nucleus (DRN) core could be at the root of the persistent action of the LH phenomenon and influences regions such as the striatum and amygdala. It is thought that the LH phenomenon could affect self-perception and it has been shown that the default mode network (DMN) is often associated with self-reflection during the resting state (RS) phase. It is therefore possible that LH influences both self-perception and the DMN. Based on previous studies, we investigated how LH affects participants. We used functional MRI (fMRI) to test this hypothesis. Participants were divided into two groups, subjected to solvable (control group), and solvable plus unsolvable (LH group) cognitive tasks. We also measured electrodermal signals, OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality scores, variables related to the anagram resolution, and related these to LH. The study revealed significant differences in the RS contrast between the two groups, with the Posterior Cingulate Cortex (PCC) (an area of the DMN) being more connected to the DRN in the LH group than in the control group, and a portion of the Superior Temporal Gyrus being more connected to the PCC in the control group than in the LH group. These results suggest that LH may have a direct impact on the DMN and could lead to the start of long-term changes.

neuroscience↗

bioRxiv2023

Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with anincreased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearsons {rho} equal to 0.43 between the observed and predicted gain, P < 1.6 x 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

genetics↗

bioRxiv2023

Construction and representation of human pangenome graphs

As a single reference genome cannot possibly represent all the variation present across human individuals, pangenome graphs have been introduced to incorporate population diversity within a wide range of genomic analyses. Several data structures have been proposed for representing collections of genomes as pangenomes, in particular graphs. In this work we collect all publicly available high-quality human haplotypes and constructed the largest human pangenome graphs to date, incorporating 52 individuals in addition to two synthetic references (CHM13 and GRCh38). We build variation graphs and de Bruijn graphs of this collection using five of the state-of-the-art tools: Bifrost, mdbg, Minigraph, Minigraph-Cactus and pggb. We examine differences in the way each of these tools represents variations between input sequences, both in terms of overall graph structure and representation of specific genetic loci. This work sheds light on key differences between pangenome graph representations, informing end-users on how to select the most appropriate graph type for their application.

bioinformatics↗

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2019

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis.

bioinformatics↗

bioRxiv2019

JASS: Command Line and Web interface for the joint analysis of GWAS results

Genome Wide Association Study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available, and studies have demonstrated their utility for a range of secondary analyses. This includes in particular the joint analysis of multiple GWAS to identify new genetic variants missed by univariate screenings. However, although several methods have been proposed, there are very few large scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package solves all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of various joint statistics, an optimized data management process, and a web interface for exploration purposes. Benchmark analyses confirmed the strong performances of JASS. We also performed multiple real data analyses demonstrating the strong potential of JASS for the detection of new associated genetic variants across various scenarios. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

bioinformatics↗